{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Position_Salaries.csv')\n",
    "X = dataset.iloc[:, 1:2].values\n",
    "y = dataset.iloc[:, 2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\satyam\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\sklearn\\utils\\validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "c:\\users\\satyam\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\sklearn\\preprocessing\\data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "c:\\users\\satyam\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\sklearn\\preprocessing\\data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Feature Scaling\n",
    "sc_X = StandardScaler()\n",
    "sc_y = StandardScaler()\n",
    "X = sc_X.fit_transform(X)\n",
    "y = sc_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting SVR to the dataset\n",
    "regressor = SVR(kernel = 'rbf')\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predicting a new result\n",
    "y_pred = regressor.predict(6.5)\n",
    "y_pred = sc_y.inverse_transform(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3wQef3Y6BAxcFwPrrNw+EplAYNKjK+xJ81pJZVXJolHj1VfjFL4pbf2mQzJkD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa573150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the SVR results\n",
    "plt.scatter(X, y, color = 'red')\n",
    "plt.plot(X, regressor.predict(X), color = 'blue')\n",
    "plt.title('Truth or Bluff (SVR)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iaLt8tM46lT2n3NxryawqOWmUmDIFfvjD8vuGDEmjja67bvr1ve667ZfXXhsG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\n+ysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf079f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the SVR results (for higher resolution and smoother curve)\n",
    "X_grid = np.arange(min(X), max(X), 0.01) # choice of 0.01 instead of 0.1 step because the data is feature scaled\n",
    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
    "plt.scatter(X, y, color = 'red')\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
    "plt.title('Truth or Bluff (SVR)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
